Contributo in atti di convegno, 2017, ENG, 10.1109/OCEANSE.2017.8084721
Ferretti Roberta, Bibuli Marco, Caccia Massimo, Chiarella Davide, Odetti Angelo, Ranieri Andrea, Zereik Enrica and Bruzzone Gabriele
Consiglio Nazionale delle Ricerche - Istituto di Studi sui Sistemi Intelligenti per l'Automazione Via De Marini 6 - 16149, Genova, Italy
This work describes the exploitation of a Remotely Operated Vehicle (ROV), equipped with a multi-parametric sensors package (acoustic and video), for the exploration and characterisation of sea-bottoms covered with Posidonia oceanica seagrass, which represents a valuable indicator of the environmental health. The data collection is achieved by the employment of a single beam echosounder and a down-looking underwater camera. An acoustic data procedural analysis based on machine learning methods was developed to automatically detect the Posidonia presence, so that in future works it will be possible to operate also in low-visibility conditions, using only the acoustic sensors. Data acquisition was carried out over different seafloor types in coastal area near Biograd Na Moru (Croatia) and the preliminary results are reported in the paper.
OCEANS 2017 - Aberdeen, pp. 1–6, Aberdeen, UK, 19-22/6/2017
Machine Learning, Posidonia Detection, unmanned marine vehicles
Chiarella Davide, Odetti Angelo, Ferretti Roberta, Caccia Massimo, Bruzzone Gabriele, Bibuli Marco, Zereik Enrica, Ranieri Andrea
ILC – Istituto di linguistica computazionale "Antonio Zampolli", IMATI – Istituto di matematica applicata e tecnologie informatiche "Enrico Magenes", INM – Istituto di iNgegneria del Mare
ID: 378509
Year: 2017
Type: Contributo in atti di convegno
Creation: 2017-11-21 15:24:57.000
Last update: 2022-02-25 15:02:57.000
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:378509
DOI: 10.1109/OCEANSE.2017.8084721
Scopus: 2-s2.0-85044633776
ISI Web of Science (WOS): 000426997000152